Artificial Neural Network (ANN) Modeling for Predicting Performance of SBS Modified Asphalt
Due to the superiorities of Styrene butadiene styrene (SBS) modified asphalt, it is widely used in civil engineering application. Meanwhile, accurately predicting and obtaining performance parameters of SBS modified asphalt in unison is difficult. At present, it is essential to discover an accurate...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/23/8695 |
| _version_ | 1797462699533139968 |
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| author | Ke Zhong Qiao Meng Mingzhi Sun Guobao Luo |
| author_facet | Ke Zhong Qiao Meng Mingzhi Sun Guobao Luo |
| author_sort | Ke Zhong |
| collection | DOAJ |
| description | Due to the superiorities of Styrene butadiene styrene (SBS) modified asphalt, it is widely used in civil engineering application. Meanwhile, accurately predicting and obtaining performance parameters of SBS modified asphalt in unison is difficult. At present, it is essential to discover an accurate and simple method between the input and output data. ANNs are used to model the performance and behavior of materials in place of conventional physical tests because of their adaptability and learning. The objective of this study discussed the application of ANNs in determining performance of SBS modified asphalt, based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) tests. A total of 150 asphalt mixtures were prepared from three matrix asphalt, two SBS modifiers and five modifier dosages. With the most suitable algorithm and number of neurons, an ANN model with seven hidden neurons was used to predict SBS content, needle penetration and softening point by using infrared spectral data of different modified asphalts as input. The results indicated that ANN-based models are valid for predicting the performance of SBS modified asphalt. The coefficient of determination (R<sup>2</sup>) of SBS content, softening point and penetration prediction models with the same grade of asphalt exceeded 99%, 98% and 96%, respectively. It can be concluded that ANNs can provide well-satisfied regression models between the SBS content and infrared spectrum statistics sets, and the precision of penetration and softening point model founded by the same grade of asphalt is high enough to can meet the forecast demand. |
| first_indexed | 2024-03-09T17:41:17Z |
| format | Article |
| id | doaj.art-128b4d596c7e42eba69d66728313cf61 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T17:41:17Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-128b4d596c7e42eba69d66728313cf612023-11-24T11:32:35ZengMDPI AGMaterials1996-19442022-12-011523869510.3390/ma15238695Artificial Neural Network (ANN) Modeling for Predicting Performance of SBS Modified AsphaltKe Zhong0Qiao Meng1Mingzhi Sun2Guobao Luo3Research Institute of Highway Ministry of Transport, Beijing 100088, ChinaSchool of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, ChinaResearch Institute of Highway Ministry of Transport, Beijing 100088, ChinaResearch Institute of Highway Ministry of Transport, Beijing 100088, ChinaDue to the superiorities of Styrene butadiene styrene (SBS) modified asphalt, it is widely used in civil engineering application. Meanwhile, accurately predicting and obtaining performance parameters of SBS modified asphalt in unison is difficult. At present, it is essential to discover an accurate and simple method between the input and output data. ANNs are used to model the performance and behavior of materials in place of conventional physical tests because of their adaptability and learning. The objective of this study discussed the application of ANNs in determining performance of SBS modified asphalt, based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) tests. A total of 150 asphalt mixtures were prepared from three matrix asphalt, two SBS modifiers and five modifier dosages. With the most suitable algorithm and number of neurons, an ANN model with seven hidden neurons was used to predict SBS content, needle penetration and softening point by using infrared spectral data of different modified asphalts as input. The results indicated that ANN-based models are valid for predicting the performance of SBS modified asphalt. The coefficient of determination (R<sup>2</sup>) of SBS content, softening point and penetration prediction models with the same grade of asphalt exceeded 99%, 98% and 96%, respectively. It can be concluded that ANNs can provide well-satisfied regression models between the SBS content and infrared spectrum statistics sets, and the precision of penetration and softening point model founded by the same grade of asphalt is high enough to can meet the forecast demand.https://www.mdpi.com/1996-1944/15/23/8695SBS modified asphaltATR-FTIRartificial neural networksSBS contentperformance parameters |
| spellingShingle | Ke Zhong Qiao Meng Mingzhi Sun Guobao Luo Artificial Neural Network (ANN) Modeling for Predicting Performance of SBS Modified Asphalt Materials SBS modified asphalt ATR-FTIR artificial neural networks SBS content performance parameters |
| title | Artificial Neural Network (ANN) Modeling for Predicting Performance of SBS Modified Asphalt |
| title_full | Artificial Neural Network (ANN) Modeling for Predicting Performance of SBS Modified Asphalt |
| title_fullStr | Artificial Neural Network (ANN) Modeling for Predicting Performance of SBS Modified Asphalt |
| title_full_unstemmed | Artificial Neural Network (ANN) Modeling for Predicting Performance of SBS Modified Asphalt |
| title_short | Artificial Neural Network (ANN) Modeling for Predicting Performance of SBS Modified Asphalt |
| title_sort | artificial neural network ann modeling for predicting performance of sbs modified asphalt |
| topic | SBS modified asphalt ATR-FTIR artificial neural networks SBS content performance parameters |
| url | https://www.mdpi.com/1996-1944/15/23/8695 |
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